America's top companies keep talking about AI – but can't explain the upsides

Original Article ↗ Hacker News Discussion ↗

AI as Layoff Justification and Changing Work

  • Several commenters see “AI” as rhetorical cover for layoffs, attrition pressure, or degrading conditions (e.g., bonus cuts for “not using enough AI”).
  • Some engineers describe their roles devolving into reviewing “AI slop” instead of creating, making work less meaningful and prompting career-change thoughts.
  • Others argue that what looks like “bullshit work” is often still skilled, but there’s broad agreement that a lot of performative, hype-driven AI work exists.

ROI, Enterprise Integration, and the 95% Failure Claim

  • A cited MIT/Project NANDA finding that ~95% of gen-AI pilots deliver no returns is widely discussed.
  • One camp reads this as evidence AI is overhyped or mostly failing; another notes the report blames poor enterprise integration and non-learning tools, not model quality.
  • Consensus: integration into workflows is hard and familiar; generic chatbots don’t adapt well to complex enterprise processes.

Why Executives Push AI

  • Some think leadership is just trying to justify already-committed spend; others with management experience push back, saying subscriptions are cancellable and salaries dominate costs.
  • A more common explanation: FOMO and competitive anxiety—fear that not adopting AI now will leave firms behind if/when it becomes a real productivity multiplier.
  • There’s skepticism that early familiarity with today’s tools will matter much if the tech changes rapidly.

Actual Utility: Coding, Search, and Internal Knowledge

  • Experiences with coding assistants are mixed: they can be great for small, constrained tasks (bash scripts, framework glue) but often waste time on larger features due to hand-holding, errors, and “intern that never learns” dynamics.
  • Some engineers find LLMs inferior to documentation and search for technical problems, especially in niche or NDA-protected domains.
  • Others report big wins in searching across fragmented internal systems and as “Google on steroids” for obscure or legal questions—though with liability caveats.

Narrow, Non-Coding Wins

  • Uses mentioned: generating internal reports to satisfy bureaucracy, summarizing legal notices, supporting ML/optimization work, and driving more documentation/API openness.
  • These are seen as incremental process improvements, not transformative “AGI” moments.

Fear, Bubbles, and Historical Analogies

  • Many compare AI to dot-com, blockchain, Second Life, and the “metaverse”: genuine underlying tech plus a likely financial bubble and herd behavior.
  • Some argue AI is clearly powerful but still missing a “smartphone moment”–like catalyst; others think it will quietly become core infrastructure without a single killer app.

LLMs, Hype, and Trust

  • Commenters complain about overconfident hallucinations and elaborate wrong answers, eroding trust.
  • There’s also meta-debate about unmarked LLM-generated comments “polluting the commons,” versus the view that prompt skill still adds human value.
  • Overall tone: AI is neither useless nor magic; it’s powerful, uneven, and currently over-marketed.